The objective of this proposal is to apply novel affinity mass spectrometry (AMS) technologies to identify protein biomarkers for pre-diabetes or type 2 diabetes. The methodology used combines micro-affinity protein capture from complex biological solutions with mass spectrometric detection. In this first phase (R21), we will screen the provided plasma and blood cell samples to identify candidate proteins, and protein profiles, that can appropriately differentiate sample origin from 1 of the 3 groups (normal, pre-diabetic and diabetic; as determined by the administration of an oral glucose tolerance test). Both plasma and blood cell samples will be assayed with affinity pipettes derivatized with poly- and monoclonal antibodies for specific protein target analysis. Additionally, groups of proteins will be analyzed via hydrophilic and hydrophobic ligands, metal chelating ligands, and other wide-specificity affinity surfaces. Each of these ligand surfaces possesses specific affinity characteristics that will result in the capture and purification of individual or whole groups of proteins. Protein variations observed as a result of qualitative and/or quantitative differences, that have statistical significance and correctly group samples according to their origin, will be viewed as potential biomarkers. In the second (validation) phase (R33), we will validate the discovered biomarker(s) via the application of the same methods and approaches to a larger group of sample cohorts through the use of high throughput parallel robotic processing. In the event that multiple protein biomarkers are validated, AMS panels for diabetes will be developed. This will be achieved through the construction of multi-analyte affinity pipettes that will selectively retrieve the targeted biomarkers that differentiate healthy from diseased states. Such panels will also be subject to the same validation process that each of its components endured. The ultimate result of this research will be validated protein biomarkers for pre-and type 2 diabetes and technology that are ready for clinical screening and diagnosis of the disease.